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Looks like there's another round of Zendesk email spam happening. I've gotten hundreds over the last half-hour.

I have been managing websites for a while and usually utilize SSH connections to login to, deploy code to, and otherwise remotely access the hosting servers.

I was recently informed that a client I work with considers that a legal risk.

If the SSH connection is set to disallow passwords and only authorize via SSH keys, how big of a risk is this?

I'm seeing individual programmers who have moved to 100% AI coding, but I'm curious as to how this is playing out for larger engineering teams. If you're on a team (let's say 5+ engineers) that has adopted Claude Code, Cursor, Codex, or some other agent, can you share how it's going? Are you seeing more LOCs created? Has PR velocity or PR complexity changed? Do you find yourself spending the same amount of time on PRs, less, or more?

We’ve been working on a GPU-first inference platform focused on predictable latency and cost control for production AI workloads.

Some of the engineering problems we ran into:

- GPU cold starts and queue scheduling - Multi-tenant isolation without wasting VRAM - Model loading vs container loading tradeoffs - Batch vs real-time inference routing - Handling burst workloads without long-term GPU reservation - Cost predictability vs autoscaling behavior

We wrote up the architecture decisions, what failed, and what worked.

Happy to answer technical questions - especially around GPU scheduling, inference optimization, and workload isolation.

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We're a YC W23 company building AI agents for engineering labs - our customers run similar analyses repeatedly, and the agent treated every session like a blank slate.

We looked at Mem0, Letta/MemGPT, and similar memory solutions. They all solve a different problem: storing facts from conversations — "user prefers Python," "user is vegetarian." That's key-value memory with semantic search. Useful, but not what we needed.

What we needed was something that learns user patterns implicitly from behavior over time. When a customer corrects a threshold from 85% to 80% three sessions in a row, the agent should just know that next time. When a team always re-runs with stricter filters, the system should pick up on that pattern. So we built an internal API around a simple idea: user corrections are the highest-signal data. Instead of ingesting chat messages and hoping an LLM extracts something, we capture structured events — what the agent produced, what the user changed, what they accepted. A background job periodically runs an LLM pass to extract patterns and builds a confidence-weighted preference profile per user/team/org.

Before each session, the agent fetches the profile and gets smarter over time. The gap as I see it:

Mem0 = memory storage + retrieval. Doesn't learn patterns.

Letta = self-editing agent memory. Closer, but no implicit learning from behavior.

Missing = a preference learning layer that watches how users interact with agents and builds an evolving model. Like a rec engine for agent personalization.

I built this for our domain but the approach is domain-agnostic. Curious if others are hitting the same wall with their agents. Happy to share the architecture, prompts, and confidence scoring approach in detail.

I’m trying to pressure-test a pattern I keep seeing in industrial and asset-heavy operations, and I’d value perspectives from people who’ve lived this.

In many environments (manufacturing, equipment rental, oilfield services, aerospace, medical devices), quality and ops work often starts outside formal systems: - inspection notes written by hand - photos on phones - voice notes from the field - emails and spreadsheets coordinating fixes

ERP/QMS systems exist, but under time pressure the work happens elsewhere first. When an audit, customer escalation, or safety question hits, teams scramble to reconstruct what actually happened from scattered artifacts.

A few questions I’m genuinely curious about: - Have you seen environments where this doesn’t happen? What made them different? - Where does reconstruction pain show up the most — audits, customer disputes, asset recertification, something else? - What information tends to get lost when work is summarized or normalized too early? - Who usually carries the burden of “proving” things are fine when something escalates?

I’m not selling anything or looking to promote a tool. I am just trying to understand where reality breaks abstraction in practice.

Would appreciate any firsthand experiences or counterexamples.

I’ve noticed that most physical scientific and graphing calculators are easily outdone in terms of performance, capability and ease of use by the likes of Desmos and the default calculators on OS’es like the iOS, Android, and Windows.

It kind of makes me wonder whether people still use physical calculators from Texas Instruments, Casio, etc

If you do, I’d love to know why and how it is different/better for you than the ones I’ve mentioned and others like them and vice verse.

Cheers!

Inspired by this Ask HN: https://news.ycombinator.com/item?id=46834977

But I'm going further back in time to see if there is anybody here who still uses slide rules?

I vibecoded a couple of iOS apps & a full SaaS (SEOZilla.ai) over the past six months and the honest answer is: my best coding agent sessions from 3-4 months ago would make great submissions. Excellent debugging, catching poor architecture choices, back-and-forth problem solving.

But lately? I mostly write product specs, make simple architecture decisions, and do QA. The agents just... handle it. Across the board, Opus, Sonnet, Cursor, whatever you're using, the jump in the last 2 months has been wild.

Which raises a genuine question: what is YC actually selecting for with this prompt? The most impressive sessions are probably from people using worse tools or tackling harder problems. The founders who've figured out the best workflows might have the most boring transcripts.

Anyone else finding that their "best" agent sessions are now the least interesting ones?

Hi HN,

I have a 3D printer but I’m pretty bad at CAD. Using LLMs for coding has worked extremely well for me, so I’ve been trying to apply a similar workflow to CAD/modeling.

For simple functional parts (jigs, brackets, adapters, small fixtures), I can use an LLM + OpenSCAD in a loop: it writes OpenSCAD, I compile/render, I render a few views, the LLM “looks” at the images, and we iterate until it looks right. This is already helpful, but it hits a ceiling quickly. Anything beyond simple parametric primitives becomes painful (complex geometry, precise interfaces, assemblies, tolerances/fit, etc.).

I’m curious about two things. First: any intuition on when we’ll be able to generate models at a professional level, comparable to what LLMs can do for coding right now? Second: what will that workflow look like in practice? Will it stay mostly parametric (OpenSCAD / constraints), or will it look more like an interactive “CAD copilot” inside tools like SolidWorks/Fusion that can edit the feature tree via screenshot + click style interaction? Or something else entirely, like text turning into a full feature history with constraints and checks.

If you’re already doing this, what tools/workflows are giving the best results today, and where do they fail?

Thanks!

"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it"

This has been a timely PSA.

My Google account has been suspended from GCP since March 2024.

I have submitted multiple appeals through ts-consult@google.com over 2 years. Every time I get the same automated template asking me to explain, I reply with details, then nothing. No human ever responds.

Case: #1-8622000037271

Timeline: - March 2024: Suspended, appeal submitted - April 2024: Automated requests for info, I replied - Nov 2024: More automated emails, I replied again - Dec 2024 - now: Complete silence

I am a CS researcher at UC Berkeley. This has seriously impacted my work.

Has anyone successfully gotten Google to review a GCP suspension appeal? How do you reach a human?

I came across this "Who Runs the Internet" graphic (https://upload.wikimedia.org/wikipedia/commons/e/ed/Who-Runs-the-Internet-graphic.png) when planning a blog post and it got me thinking about the role of internet service providers.

Obviously this is a highly simplified question. Internet governance is a complex topic, but from your experience and perspective, are ISPs "evil"? Who really has the power over the Internet?

I'm curious how others are handling this: I use Claude for some tasks, Cursor for coding, ChatGPT for research, and Perplexity for quick lookups.

The problem is none of them know what I've discussed with the others.

I find myself re-explaining the same context repeatedly, or copy-pasting from Notion docs.

For those of you heavily using AI tools:

- How are you managing shared context across tools?

- What's your current workflow for keeping AI "memory" consistent?

- Have you found any solutions that work well?

Especially interested in hearing from teams where multiple people need to access the same knowledge base across different AI sessions.

Hey HN,

I'm on a quest for a distraction-free writing device and considering a super cheap laptop which I can just run vim/nano on.

I'd like: - Excellent battery life - Good keyboard - Sleep/wake capabilities (why is this so hard with Linux?)

I'm thinking some kind of chromebook? Maybe an old thinkpad?

Running OpenClaw with Anthrophic API and it burned through ~USD 50 in one day.

What are other OpenClaw users seeing? Anyone found effective ways to cut costs (model tiering, caching, etc.)?

Wanted to gather some stories about people who were fired because of AI. Not a generic "reorg", what they say in the press release, but honestly, because of AI. Proves?

I've been working on an SMTP/email server lately, and while Google and some others imply a lot of frowny-faces and put quotation marks around tests "passing" for not using a corporate relay, does at least let me communicate with the broader ecosystem.

Phone calls, however, seem like a tougher nut to crack. SIP URIs would let me kinda-sorta communicate with the broader ecosystem, but many phones and software seem to have dropped support for it; only a tiny % of those typically using the PSTN (that is, a "normal phone") would be able to call or receive calls to/from my addresses -- but it would be able to be directly and neatly integrated into the email server, which is a big plus.

I will probably still implement SIP URIs and VOIP support into the email server on principle, but I wonder if anyone has any alternatives to consider. Ideally, I would be able to communicate on the PSTN, but this seems like a lost cause.

Also curious about anyone using VOIP for work whether or not they allow or block SIP URIs from external networks. I maintained our VOIP server at work more than a decade ago, but it was a "side-project" due to working at an SMB where I wore many hats and couldn't specialize in anything; I wasn't even aware of SIP URIs at the time.

Currently the GitHub status page says there is a "Major Outage" for GitHub Actions.

https://www.githubstatus.com/

This is as of 19:58 UTC / 11:58 PST on 2-Feb-2026

Facebook's feed is nonfunctional. Only some people get notification, even though they have notification for all messages turned on. Only some get the newest posts in their main feed. Sometimes I do get notifications, but only long after an urgent message was posted.

Has anybody successfully moved their local community off of Facebook groups?

I'm thinking about neighbor conversations/events, daycare, kindergarten, kids' classmates, sporting communities, etc.

If so, where did you go? Did you build something yourself or do you self-host some open source project? Did you find a good paid alternative?

What is the common thread among tech debt-crippled companies you've worked in? Not enough man hours to pay it down, difficulty to align engineering's willingness to pay it down with product's focus on new features, or something else? How can it be that, in the age of AI code gen, we haven't yet managed to solve this once and for all?

Hi everyone,

I’m building Persona, a platform to delegate email scheduling to AI. Lately, I’ve been working hard to get those first users on board, but it’s been quite challenging.

I’ve already tried the typical strategies that everybody talks about: cold email, LinkedIn InMail, careful targeting, decent copy. It’s mostly been a dead end. Low open rates, almost no replies.

At this point, I’m not looking for the usual advice you see in blog posts or on reddit. I’m specifically curious about unconventional or non-obvious things that actually worked for you early on, especially things that felt a bit scrappy, weird, or counterintuitive at the time.

If you’ve been through this phase, what genuinely worked and got you your first users?

I feel crazy to ask. Over my lifetime, I have seen endless bookmark and read-it-later apps come and go. I've done research today, and most of the things I come across are dead and gone, or seem abandoned somehow. I'm aware of Instapaper. I haven't tried it (yet).

Here are some thoughts on what might fit my personal taste: - lightweight - very cheap - self-hosting might be nice, since I have a VPS currently - I'd like to easily dump an open tab into a backlog, and get reminded about it later: maybe I go to the app, maybe I get a daily email of suggestions. If I don't feel like reading the page, I can "snooze" it or otherwise put it back in the backlog (or drop it)

I think that's all I really want. I don't need notes or AI summaries or multiple apps for multiple devices, etc.

I might just build it, but curious if anyone has something they love.

Thanks!

Especially interested in people using AI for brownfield development, but generally interested in if people are continuing down the spec driven path, or if agents + skills/prompts/mcp/agents.md/something else is filling the niche spec driven development was trying to capture.

Question was prompted by seeing spec kit have no commits for over a month an no obvious integration with GitHub’s new agents integration.

okay, so this is not so big to many of you , but today , i was just bored and tired of doing any marketing , cos nothing seemed to have been working. so i decided to do a search (i searched for quite a number of keywords) like error tracking for supabase , error tracking for next Js , but my saas didnt rank (if you dont know what im building , im working on a dead simple error tracker that notifies you when something breaks in prod, no bloated dashboards or config hell) , i built this cos sentry had too much noise and i just wanted something that lets me know when there's an issue in prod.

so i decided to do a search on "error tracking for shipfast(by marc lou)" and guess what ?? Bugmail was the first recommended result , now i dont know how this worked, i mean ive done some seo and stuff but i wasnt expecting it , and now i feel like im back , which is funny cos i didnt make a sale , i didnt onboard a new user yet i feel like o have conquered , life of a founder , i guess

i just wanted to share this here, also if you have any advice on how to rank higher on GSC and how to nail this marketing thing , any advice or feedback will be valuable

in the meantime , incase you want to check out what ive built;

you can check it out here ; https://www.bugmail.site

Status page shows it's up (https://status.signal.org).

Edit: status now updated to "Signal is experiencing technical difficulties. We are working hard to restore service as quickly as possible.")

I am curious I am not sure if AI is just hype, I use it for software and a few other things. But looking at so many people talking about AGI when the best models can't even answer simple stuff correctly, fail at tool use, are vulnerable to all types of injection attacks that don't make sense.

I don't know if the investments in AI are worth it but am I blind for not seeing any hope for AGI any time soon.

Agentic AI is interesting perhaps but I hardly have had it work perfectly, I have to hold it's hand at everything.

People making random claims about AGI soon is really weakening my confidence in AI in general. Given I haven't seen much improvements in last few years other than better tools and wrappers, and models that work better with these tools and wrappers.